A Class of Superlinearly Convergent Feasible Methods for Nonlinearly Constrained Optimizations

Abstract:

      This paper presents a class of feasible methods containing some of parameters for solving nonlinear inequality constrained optimization problem. At each iteration, the algorithm generates the search directions by solving only one quadratic programming with small scale.Under some assumptions, we prove that the algorithm possesses global and superlinear convergence.